Unification of support vector machines and soft computing paradigms for pattern recognition

Ying Li; Licheng Jiao

doi:10.1117/12.441691

20 September 2001 Unification of support vector machines and soft computing paradigms for pattern recognition

Ying Li, Licheng Jiao

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441691
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

This paper analyzes support vector machines (SVMs) and several commonly used soft computing paradigms for pattern recognition including neural and wavelet networks, and fuzzy systems. Bayesian classifiers, fuzzy partitions, etc and tries to outline the similarities and differences among them. Support vector machines provide a new approach to the problem of pattern recognition with clear connections to the underlying statistical learning theory. We try to bring SVMs into the framework of the unification paradigm called the weighted radial basis function paradigm. Unifying different classes of methods has enormous advantages, such as the ability to merge all such techniques within the same system. It is hoped that this paper would provide theoretical guides for the study and applications of support vector machine and soft computing paradigms.

Citation Download Citation

Ying Li and Licheng Jiao "Unification of support vector machines and soft computing paradigms for pattern recognition", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441691

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